Recently, with the development of robot technology, mobile robots which can set the movement route by themselves have been utilized. In order to allow such mobile robots to efficiently determine a position and move in a space, it is required that the mobile robot be able to recognize its own position in the space while generating a map for the space wherein the robot moves.
Mobile robots employ a gyroscope and a driving motor equipped with an encoder to drive by dead reckoning navigation, and generate a map by analyzing images taken using a camera provided in the upper portion. In this case, when an error is incurred in the driving information from the gyroscope and the encoder, the image information obtained from the camera is utilized to correct the accumulated error.
However, location-based mobile robots which have been developed until now were developed under the assumption of movement on a two-dimensional plane using a monocular camera or laser scanner. However, when a monocular camera is used, it is difficult to determine the distance to a feature point. Therefore, as the error of the dead reckoning navigation is increased, increasingly many errors may be included in the position recognition results.
Further, since laser scanners are too expensive to apply to mobile robots, studies for utilizing a simultaneous localization and mapping (SLAM) technology, which recognizes a space and updates the location using a stereo camera, have been increasing in recent years.
Mobile robots based on SLAM technology perform the processes of extracting a corner feature point from an image, creating a map by restoring three-dimensional coordinates of the corner feature point, and recognizing a location.
It is very important for mobile robots to recognize a space and recognize their own position in the space. Since mobile robots which do not employ the above-described technique have limited mobility and may provide a very limited type of service, mobile robots are being competitively developed.